---
language:
  - pl
license: apache-2.0
library_name: transformers
pipeline_tag: text-classification
tags:
  - text-classification
  - sentiment-analysis
  - twitter
  - distiluse
base_model: sentence-transformers/distiluse-base-multilingual-cased-v1
datasets:
  - tweet_eval
metrics:
  - f1
  - accuracy
  - precision
  - recall
widget:
  - text: "Szczęście i Opatrzność mają znaczenie Gratuluje @pzpn_pl"
    example_title: "Example 1"
  - text: "Osoby z Ukrainy zapłacą za życie w centrach pomocy? Sprzeczne prawem UE, niehumanitarne, okrutne."
    example_title: "Example 2"
  - text: "O której kończycie dzisiaj?"
    example_title: "Example 3"
model-index:
  - name: twitter-sentiment-pl-fast
    results:
      - task:
          type: text-classification
          name: Sentiment Analysis
        dataset:
          name: TweetEval (translated to Polish)
          type: tweet_eval
        metrics:
          - type: f1
            value: 0.570
            name: F1 (macro)
          - type: precision
            value: 0.570
            name: Precision (macro)
          - type: recall
            value: 0.575
            name: Recall (macro)
          - type: accuracy
            value: 0.582
            name: Accuracy
---


# Twitter Sentiment PL (fast)

Twitter Sentiment PL (fast) is a model based on [distiluse](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v1) for analyzing sentiment of Polish twitter posts. It was trained on the translated version of [TweetEval](https://www.researchgate.net/publication/347233661_TweetEval_Unified_Benchmark_and_Comparative_Evaluation_for_Tweet_Classification) by Barbieri et al., 2020 for 10 epochs on single RTX3090 gpu

The model will give you a three labels: positive, negative and neutral.

## How to use

You can use this model directly with a pipeline for sentiment-analysis:

```python
from transformers import pipeline

nlp = pipeline("sentiment-analysis", model="bardsai/twitter-sentiment-pl-fast")
nlp("Szczęście i Opatrzność mają znaczenie Gratuluje @pzpn_pl")
```
```bash
[{'label': 'positive', 'score': 0.9965680837631226}]
```

## Performance
| Metric | Value |
| --- | ----------- |
| f1 macro | 0.570 |
| precision macro | 0.570 |
| recall macro | 0.575 |
| accuracy | 0.582 |
| samples per second | 225.9 |

(The performance was evaluated on RTX 3090 gpu)

## Changelog
- 2023-07-19: Initial release

## License

This model is released under the **[Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0)**, inherited from the base model [sentence-transformers/distiluse-base-multilingual-cased-v1](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v1) (Apache 2.0).

Attribution: distiluse-base-multilingual-cased-v1 — Sentence-Transformers (UKP Lab); Twitter Sentiment PL (fast) — bards.ai.

## About bards.ai

At bards.ai, we focus on providing machine learning expertise and skills to our partners, particularly in the areas of nlp, machine vision and time series analysis. Our team is located in Wroclaw, Poland. Please visit our website for more information: [bards.ai](https://bards.ai/)

Let us know if you use our model :). Also, if you need any help, feel free to contact us at info@bards.ai